System and method for segmenting a sentence

ABSTRACT

Embodiments of the disclosure provide systems and methods for segmenting a sentence. The method may include identifying a first phrase in the sentence associated with a first segmentation path, determining a first group of derivative phrases semantically associated with the first phrase, determining a first evaluation score based on modified sentences generated by replacing the first phrase with the respective derivative phrase in the first group, and segmenting the sentence based on the first evaluation score.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2017/095305, filed on Jul. 31, 2017, the contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to Text-to-Speech (TS) techniques, andmore particularly, to segmenting a text sentence.

BACKGROUND

Text-to-Speech techniques can transcribe text information into audiosignals. For example, in a navigation application (e.g., a DiDi app),text information, such as traffic condition, addresses, or the like maybe presented to a user by voice. And books and news may be read to theuser as well by means of TS techniques.

To be read in a natural way, a piece of text (e.g., a sentence) must besegmented properly before being transcribed into audio signals.Generally, each of the phrases that are included in a sentence containsone or more words. Consistent with this disclosure, a word can be anEnglish, French, Spanish, etc. word in the Latin language, or acharacter in Asian languages such as Chinese, Korean, Japanese, etc.These words or characters may be segmented into phrases in a pluralityof possible combinations. In Example I, a sentence “The man over thereis watching TV” may be segmented according to two segmentation paths asbelow:

A first segmentation path: “The man/ over/ there is/ watching TV”.

A second segmentation path: “The man/ over there/ is/ watching TV”.

When the audio signals are generated based on the first segmentationpath, the transcription may make no sense to the user. However,conventional segmentation systems and methods cannot determine whichsegmentation path is better, as each phrase in both segmentation pathsseems linguistically reasonable.

To address the above issue, embodiments of the disclosure providedimproved systems and methods for segmenting a sentence.

SUMMARY

An aspect of the disclosure is directed to a method for segmenting asentence. The method may include identifying, by a processor, a firstphrase in the sentence associated with a first segmentation path;determining, by the processor, a first group of derivative phrasessemantically associated with the first phrase; determining, by theprocessor, a first evaluation score based on modified sentencesgenerated by replacing the first phrase with the respective derivativephrase in the first group; and segmenting the sentence based on thefirst evaluation score.

Another aspect of the disclosure is directed to a system for segmentinga sentence. The system may include a communication interface configuredfor receiving the sentence; a memory configured for storing the sentenceand a language model; and a processor configured for identifying a firstphrase in the sentence associated with a first segmentation path;determining a first group of derivative phrases semantically associatedwith the first phrase; determining a first evaluation score based onmodified sentences generated by replacing the first phrase with therespective derivative phrase in the first group; and segmenting thesentence based on the first evaluation score.

Yet another aspect of the disclosure is directed to a non-transitorycomputer-readable medium that stores a set of instructions, whenexecuted by at least one processor of a segmentation device, cause thesegmentation device to perform a method for segmenting a sentence. Themethod may include identifying a first phrase in the sentence associatedwith a first segmentation path; determining a first group of derivativephrases semantically associated with the first phrase; determining afirst evaluation score based on modified sentences generated byreplacing the first phrase with the respective derivative phrase in thefirst group; and segmenting the sentence based on the first evaluationscore.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary system for segmenting asentence, according to some embodiments of the disclosure.

FIG. 2 illustrates two exemplary segmentation paths of a Chinesesentence, according to some embodiments of the disclosure.

FIG. 3 is a flowchart of an exemplary method for segmenting a sentence,according to some embodiments of the disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the exemplary embodiments,examples of which are illustrated in the accompanying drawings. Whereverpossible, the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

FIG. 1 is a block diagram of an exemplary system 100 for segmenting asentence, according to some embodiments of the disclosure.

System 100 may be a general server or a proprietary device forprocessing text information in a sentence. As shown in FIG. 1, system100 may include a communication interface 102, a processor 104, and amemory 116. Processor 104 may further include multiple functionalmodules, such as a tokenizer 106, a phrase identifier 108, a derivativephrase generator 110, a score determination unit 112, and a segmentationunit 114. These modules (and any corresponding sub-modules or sub-units)can be functional hardware units (e.g., portions of an integratedcircuit) of processor 104 designed for use with other components or apart of a program. The program may be stored on a computer-readablemedium, and when executed by processor 104, it may perform one or morefunctions. Although FIG. 1 shows units 106-114 all within one processor104, it is contemplated that these units may be distributed amongmultiple processors located near or remotely with each other. System 100may be implemented in the cloud, or a separate computer/server.

Communication interface 102 may be configured to receive one or moresentences 120. Memory 116 may be configured to store the one or moresentences. Memory 116 may be implemented as any type of volatile ornon-volatile memory devices, or a combination thereof, such as a staticrandom access memory (SRAM), an electrically erasable programmableread-only memory (EEPROM), an erasable programmable read-only memory(EPROM), a programmable read-only memory (PROM), a read-only memory(ROM), a magnetic memory, a flash memory, or a magnetic or optical disk.

Consistent with embodiments of the disclosure, processor 104 mayidentify a first phrase in the sentence received by communicationinterface 102. The first phrase is associated with a first segmentationpath.

For example, tokenizer 106 may segment a sentence in one or moresegmentation paths. As described with Example I, “The man over there iswatching TV” may be segmented as a first segmentation path of “The man/over/ there is/ watching TV” or a second segmentation path of “The man/over there/ is/ watching TV.” A first phrase “there is” is associatedwith the first segmentation path and a second phrase “over there” isassociated with the second segmentation path. Phases “there is” and“over there” may be identified by comparing the first and secondsegmentation paths and locating differences between the segmentationpaths. With reference to Example I, phrase identifier 108 may identifythat the segments “over/ there is” and “over there/ is” are notidentical by comparing the first and second segmentation paths. Thenphrase identifier 108 may further identify “there is” as a first phraseassociated with the first segmentation path and “over there” as a secondphrase associated with the second segmentation path. It is contemplatedthat the first and second segments are different segments on a same partof the sentence. Therefore, the first and second phrases has at leastone overlapping word (e.g., “there”).

Derivative phrase generator 110 may then determine a first group ofderivative phrases semantically associated with the first phrase. Thederivative phrases may be determined based on semantic vectors betweenthe first phrase and candidate phrases. The candidate phrases may bepre-stored in a phrase database, and each phrase in the database may becompared with the first phrase based on the semantic vectors. Thesemantic vector is an algebraic model for representing text documents(e.g., a phrase) as vectors. Generally, the difference between twosemantic vectors associated with phrases may be represented by a cosinedistance between the two semantic vectors. In some embodiments, thefirst group of derivative phrases may include synonyms of the firstphrase. As shown in FIG. 1, communication interface 102 may be furtherconfigured to retrieve from a phrase database 122 synonyms correspondingto the phrases. For example, the first phrase “there is” may haveseveral synonyms, such as “there are”, “here is”, “exist”, “have”,“has”, or the like. In some embodiments, processor 104 may furtherdetermine a second group of derivative phrases semantically associatedwith the second phrase. The second group of derivative phrasesassociated with the second phrase “over there” may have synonyms, suchas “there”, “over here”, or the like.

Given the first group of derivative phrases, score determination unit112 may replace the first phrase with the respective derivative phrasesin the first group, and determine a first evaluation score based on themodified sentences. For example, the sentence “The man/ over/ there is/watching TV” may be modified by replacing the first phrase “there is”with the synonyms of “there are”, “here is”, “exist”, “have”, “has”, orthe like. Therefore, a plurality of modified sentences may be generated,including “The man/ over/ there are/ watching TV”, “The man/ over/ hereis/ watching TV”, “The man/ over/ exist/ watching TV”, “The man/ over/have/ watching TV”, “The man/ over/ has/ watching TV”, or the like. Insome embodiments, a language model score may be determined for each ofthe modified sentences using a language model, and the language modelscores may be averaged to derive the first evaluation score. Thelanguage model can evaluate a segmentation path according to naturallanguage rules. In some embodiments, the modified sentences “The man/over/ there are/ watching TV”, “The man/ over/here is/ watching TV”,“The man/ over/ exist/ watching TV”, “The man/ over/ have/ watching TV”,“The man/ over/ has/ watching TV” may be respectively evaluated as 47points, 68 points, 35 points, 33 points, and 42 points. Accordingly, thefirst evaluation score may be an average of the above scores, i.e., 45points. As an example, for the modified sentence “The man/ over/ thereare/ watching TV”, the language model may determine that a singularsubject going with a plural verb does not comply with the naturallanguage rules, and therefore evaluate the modified sentence with a lowscore. It is contemplated that, whether a low score indicates asegmentation path as being improper or proper is not restrictive.

The original sentence including the first phrase may also be evaluatedby the language model to generate a first base score. For example, theoriginal sentence “The man/ over/ there is/ watching TV” may beevaluated as 68 points, which is considered as the first base score.

Similarly, score determination unit 112 may replace the second phrase(e.g., “over there”) with the respective derivative phrases in thesecond group, and determine a second evaluation score based on themodified sentences. For example, a second evaluation score may bedetermined as 67 points and a second base score may be determined as 69points.

The language model may be trained for a designated language, such asEnglish, Chinese, Japanese, or the like. For sentences in Example Idescribed above, an English language model may be used. The languagemodel may be a general model pre-stored in memory 116, or trained for aspecific area (e.g., novel, legal, navigation, or the like) by, forexample, machine learning.

It contemplated that, when a phrase of a sentence within a propersegmentation path is modified with a synonym of the phrase, the modifiedsentence should still be able to deliver similar meanings and follownatural language rules. That is, when the language model evaluates thesegmentation paths based on the original sentence and modified sentence,the language model scores based on the original sentence and themodified sentence should be close. On the other hand, when a phrasewithin an improper segmentation path is replaced with a synonym of thephrase, the modified sentences may have drastically different meaningsor even does not meet natural language rules. That is, a differencebetween the language model scores based on the original sentence and themodified sentence may be magnified, so that processor 104 may determinethe segmentation path is not proper. In some embodiments, a thresholdmay be pre-determined, such as 5 points. When the difference is greaterthan or equal to the threshold, the corresponding segmentation path maybe determined as an improper segmentation path. It is contemplated that,the pre-determined difference may be different for different languagemodels.

Segmentation unit 114 may further segment the sentence based on theevaluation score (e.g., the first and/or second evaluation score). Forexample, according to the first segmentation path, a difference betweenthe first base score (e.g., 68 points) and the first evaluation score(e.g., 45 points) may be determined. The difference is 68−45=23 points,which is greater than the threshold (e.g., 5 points). Therefore,processor 104 may determine that the first segmentation path is animproper segmentation path. On the other hand, according to the secondsegmentation path, a difference between the second base score (e.g., 69points) and the second evaluation score (e.g., 67 points) may bedetermined. The difference is 69−67=2 points, which is less than thethreshold (e.g., 5 points). Therefore, segmentation unit 114 maydetermine that the second segmentation path is a proper segmentationpath, and segment the sentence according to the second segmentationpath.

As discuss above, the first base score (e.g., 68) corresponding to thefirst segmentation path and the second base score (e.g., 69)corresponding to the second segmentation path may be close, andselecting one segmentation path over another based on these base scoresmay sometimes cause errors. That is, a base score of 69 does notnecessarily indicate that the corresponding segmentation path is betterthan one that has a base score of 68. However, by replacing theidentified phrases in the first and second segmentation paths withsynonyms, the difference between the first and second segmentation pathsmay be magnified so that segmentation unit 114 may more accuratelyselect one of the segmentation paths based on the evaluation scoresassociated with respective segmentation paths.

Consistent with embodiments of the disclosure, segmentation unit 114 maysegment the sentence based on a difference between the first evaluationscore and the second evaluation score. For example, as described above,the first and second evaluation scores are 45 points and 67 points,respectively. Therefore, segmentation unit 114 may select one of thefirst and second segmentation paths that has the higher evaluation score(e.g., the second segmentation path), and segment the sentence accordingto the selected segmentation path.

It is contemplated that, all the scores and threshold described aboveare merely illustrative, and may be modified if necessary.

Besides Example I in English, system 100 may also process a sentence oftext in another language, such as Chinese. FIG. 2 further illustratestwo exemplary segmentation paths of a Chinese sentence, according tosome embodiments of the disclosure. The Chinese sentence means thatAssociazione Calcio Fiorentina has the ability to compete with JuventusFootball Club in Italian Serie A League. Pin′yin spelling is marked foreach Chinese character under the original Chinese sentence.

As shown in FIG. 2, in Example II, the Chinese sentence may be segmentedaccording to first and second segmentation paths. The first and secondsegmentation paths may be compared by tokenizer 106 to identify thatsegment 202 and segment 204 are different. Then phrase identifier 108may identify the phrase corresponding to “Yi Jia” in first segmentationpath as a first phrase associated with the first segmentation path, andthe phrase “Yi Jia” means “Italian Serie A League” in Chinese. Phraseidentifier 108 may also identify the phrase corresponding to “Zai Yi” inthe second segmentation path, and the phrase “Zai Yi” means “care for”,“mind”, or the like. It can be noticed that, the phrase “Yi Jia” and thephrase “Zai Yi” have a same Chinese character “Yi” in common.

Derivative phrase generator 110 may determine a first group and a secondgroup of derivative phrases semantically associated with the firstphrase and the second phrase, respectively. For example, the derivativephrases semantically associated with the first phrase “Yi Jia” mayinclude “Primera División de España,” “Premie League,” “GermanBundesliga,” or the like. And the derivative phrases semanticallyassociated with the second phrase “Zai Yi” may include “care for,”“mind,” “care about,” or the like.

By replacing the first and second phrases with the correspondingderivative phrases respectively, modified sentences may be generated.And score determination unit 112 may then evaluate the first and secondsegmentation paths based on the original sentence and the modifiedsentences using a language model for Chinese. For example, the first andsecond segmentation paths based on the original sentence may be scoredas 58.342 and 59.081 respectively, and the first and second segmentationpaths based on the modified sentences may be scored as 58.561 and34.952.

Based on the evaluation scores, segmentation unit 114 may select thefirst segmentation path having a higher evaluation score (e.g., 58.561as opposed to 34.952), and segment the sentence accordingly. Note inthis example, selecting the segmentation path based on the base scoreswould have resulted in an error, as the second segmentation pathactually has a slightly higher base score.

The above-described system 100 may magnify (and sometimes, reverse) thedifference between two or more segmentation paths by replacing anidentified phrase with synonyms and select a proper path for segmentingthe sentence, when the language model cannot distinguish them.

Another aspect of the disclosure is directed to a method for segmentinga sentence. FIG. 3 is a flowchart of an exemplary method 300 forsegmenting a sentence, according to some embodiments of the disclosure.For example, method 300 may be implemented by a segmentation device, andmay include steps S302-S308.

In step S302, the segmentation device may identify a first phrase in thesentence associated with a first segmentation path. In some embodiments,the segmentation device may generate at least two segmentation paths ofthe sentence. Each of the segmentation path may include a plurality ofsegments. The segmentation device may compare the plurality of segmentsand identify a segment in a first segmentation path that is differentfrom a corresponding segment in other segmentation paths. Similarly, thesegmentation device may identify a segment in a second segmentationpath. Further, the segmentation device may identify the first phrase inthe sentence associated with the first segmentation path, and the secondphrase in the sentence associated with the second segmentation path. Asthe first and second segmentation paths are different segmentation pathson a same sentence or a same part of the sentence, the first and secondphrases may have at least one overlapping word.

In step S304, the segmentation device may determine a first group ofderivative phrases semantically associated with the first phrase. Thederivative phrases may be determined based on semantic vectors betweenthe first phrase and candidate phrases. The candidate phrases may bepre-stored in a phrases database, and each phrase in the database may becompared with the first phrase based on the semantic vectors. In someembodiments, the first group of derivative phrases may include synonymsof the first phrase. Similarly, the segmentation device may determine asecond group of derivative phrases semantically associated with thesecond phrase.

In step S306, the segmentation device may determine a first evaluationscore based on the modified sentences generated by replacing the firstphrase with the respective derivative phrases in the first group. Alanguage model score may be determined for each of the modifiedsentences using a language model, and the language model scores may beaveraged to derive the first evaluation score. The language model mayevaluate a segmentation path according to natural language rules. Thesegmentation device may further determine a second evaluation scorebased on the modified sentences generated by replacing the second phrasewith the respective derivative phrases in the second group. A first basescore and a second base score may be also determined based the sentence.For example, the first and second segments of the sentence may be scoredby the language model to generate the first and second base scores. Itis contemplated that, an averaged score is merely an example forevaluating the segmentation paths. The individual scores may bemanipulated or combined in any suitable ways to derive the evaluationscore. For example, instead of a straight average of the individualscores, the evaluation score may be a weighted average of the individualscores, and the weights may correspond to how close the respectivesynonyms are to the phrase. As another example, a variance of theindividual language model scores for the modified sentences may be usedto determine whether the language model scores vary significantly. Ifthe language model scores vary significantly, it may indicate thecorresponding segmentation path is not proper.

In step S308, the segmentation device may segment the sentence based onthe first evaluation score. In one embodiment, the segmentation devicemay compare the first evaluation score with the first base score, andsegment the sentence according to the first segmentation path when thedifference between the first base score and the first evaluation scoreis less than a threshold. As discussed above, when a sentence issegmented properly, the evaluation score based on modified sentencesshould be close to the base score based on the original sentence. Inanother embodiment, the segmentation device may segment the sentencebased on a difference between the first evaluation score and the secondevaluation score. For example, the segmentation device may select one ofthe first and second segmentation paths that has the higher evaluationscore, and segment the sentence according to the selected segmentationpath.

Yet another aspect of the disclosure is directed to a non-transitorycomputer-readable medium storing instructions which, when executed,cause one or more processors to perform the methods, as discussed above.The computer-readable medium may include volatile or non-volatile,magnetic, semiconductor, tape, optical, removable, non-removable, orother types of computer-readable medium or computer-readable storagedevices. For example, the computer-readable medium may be the storagedevice or the memory module having the computer instructions storedthereon, as disclosed. In some embodiments, the computer-readable mediummay be a disc or a flash drive having the computer instructions storedthereon.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the disclosed segmentationsystem and related methods. Other embodiments will be apparent to thoseskilled in the art from consideration of the specification and practiceof the disclosed system and related methods. Although the embodimentsare described for two segmentation paths as an example, the describedsegmentation system and method can be applied to more than twosegmentation paths.

It is intended that the specification and examples be considered asexemplary only, with a true scope being indicated by the followingclaims and their equivalents.

What is claimed is:
 1. A computer-implemented method for segmenting asentence, comprising: identifying a first phrase in the sentenceassociated with a first segmentation path; determining a first group ofderivative phrases semantically associated with the first phrase;determining a first evaluation score based on modified sentencesgenerated by replacing the first phrase with the respective derivativephrase in the first group; identifying a second phrase in the sentenceassociated with a second segmentation path; determining a second groupof derivative phrases semantically associated with the second phrase;determining a second evaluation score based on score based on modifiedsentences generated by replacing the second phrase with the respectivederivative phrase in the second group; and segmenting the sentence basedon a difference between the first evaluation score and the secondevaluation score.
 2. The method of claim 1, wherein the determining thefirst evaluation score includes: determining a language model score foreach modified sentence; and averaging the language model scores toderive the first evaluation score.
 3. The method of claim 2, wherein thelanguage model score is generated using a language model according tonatural language rules.
 4. The method of claim 1, further comprising:determining a first base score based on the sentence including the firstphrase; and segmenting the sentence based on a difference between thefirst base score and the first evaluation score, wherein the sentence issegmented according to the first segmentation path when the differencebetween the first base score and the first evaluation score is less thana threshold.
 5. The method of claim 1, wherein the second phrase has atleast one overlapping word with the first phrase.
 6. The method claim 1,further comprising: identifying the first phrase and the second phrasebased on a difference between the first segmentation path and the secondsegmentation path.
 7. The method claim 1, further comprising: selectingone of the first and second segmentation paths that has the higherevaluation score; and segmenting the sentence according to the selectedsegmentation path.
 8. The method of claim 1, wherein the first group ofderivative phrases are determined based on semantic vectors between thefirst phrase and candidate phrases.
 9. A system for segmenting asentence, comprising: a communication interface configured for receivingthe sentence; a memory configured for storing the sentence and alanguage model; and a processor configured for identifying a firstphrase in the sentence associated with a first segmentation path;determining a first group of derivative phrases semantically associatedwith the first phrase; determining a first evaluation score based onmodified sentences generated by replacing the first phrase with therespective derivative phrase in the first group; identifying a secondphrase in the sentence associated with a second segmentation path;determining a second group of derivative phrases semantically associatedwith the second phrase; determining a second evaluation score based onscore based on modified sentences generated by replacing the secondphrase with the respective derivative phrase in the second group; andsegmenting the sentence based on a difference between the firstevaluation score and the second evaluation score.
 10. The system ofclaim 9, wherein the processor is further configured for: determining alanguage model score for each modified sentence; and averaging thelanguage model scores to derive the first evaluation score.
 11. Thesystem of claim 10, wherein the language model score is generated usinga language model according to natural language rules.
 12. The system ofclaim 9, wherein the processor is further configured for: determining afirst base score based on the sentence including the first phrase; andsegmenting the sentence based on a difference between the first basescore and the first evaluation score, wherein the sentence is segmentedaccording to the first segmentation path when the difference between thefirst base score and the first evaluation score is less than athreshold.
 13. The system of claim 9, wherein the second phrase has atleast one overlapping word with the first phrase.
 14. The system claim9, wherein the processor is further configured for: identifying thefirst phrase and the second phrase based on a difference between thefirst segmentation path and the second segmentation path.
 15. The systemclaim 9, wherein the processor is further configured for: selecting oneof the first and second segmentation paths that has the higherevaluation score; and segmenting the sentence according to the selectedsegmentation path.
 16. A non-transitory computer-readable medium thatstores a set of instructions, when executed by at least one processor ofa segmentation device, cause the segmentation device to perform a methodfor segmenting a sentence, the method comprising: identifying a firstphrase in the sentence associated with a first segmentation path;determining a first group of derivative phrases semantically associatedwith the first phrase; determining a first evaluation score based onmodified sentences generated by replacing the first phrase with therespective derivative phrase in the first group; identifying a secondphrase in the sentence associated with a second segmentation path;determining a second group of derivative phrases semantically associatedwith the second phrase; determining a second evaluation score based onscore based on modified sentences generated by replacing the secondphrase with the respective derivative phrase in the second group; andsegmenting the sentence based on a difference between the firstevaluation score and the second evaluation score.